Additionally, deep learning models can also deal with various imperfections in the manifolds.
The work [1]} evaluates DNN models and is able to predict DoAs using a ULA with an imperfectly known manifold.
In the work of [2]} and [3]}, it is shown that a DNN with a denoising autoencoder performs better than classic subspace methods at low SNR, and can estimate DoA without knowing the exact number of sources
Further, it has been shown that a DNN can deal well with the limited field-of-view problem, outperforming subspace methods at low-angles [4]}. These aforementioned techniques however, do not deal well with coherent multipath interference.
However, [5]} enhances CNN performance with sparse prior information, while [6]} shows that a CNN can estimate DoAs of the main source in the presence of coherent interference, although the approach does not estimate the total number of paths or the direction of the multipath.
Our previous work [7]} demonstrated that a DNN can estimate the model and even determine if the array is overloaded.
